The behavior policy blueapplied during training was \(\varepsilon \) -greedy [1]} blueand our parameters are largely in line with the literature. \(\varepsilon \)  bluewas set to start at 1 and decay to blue\(0.05\)  over a given fraction of the training steps, where it stays fixed. blueIn practice this fraction was set to \(0.75\)  when the target domain was an attractor, and \(0.1\)  and \(0.5\)  when the target was a pre-assigned subset of the state space (depending on the PBN).
The selected discount factor \(\gamma \)  was set to \(0.99\)  to weigh future rewards strongly, as the agent may direct the network to the desired state after a large number of steps.
